diff --git a/.github/workflows/manubot.yaml b/.github/workflows/manubot.yaml index 1ef5e98..4e93327 100644 --- a/.github/workflows/manubot.yaml +++ b/.github/workflows/manubot.yaml @@ -45,6 +45,7 @@ jobs: # Set SPELLCHECK to true/false for whether to check spelling in this action. # For workflow dispatch jobs, this SPELLCHECK setting will be overridden by the user input. SPELLCHECK: true + BUILD_DOCX: true defaults: run: shell: bash --login {0} diff --git a/README.md b/README.md index d59814b..8f6c7aa 100644 --- a/README.md +++ b/README.md @@ -27,5 +27,6 @@ If you would like to be recognized as a co-author, please add your details to th + [Markdown Formatting Cheat Sheet](content/formatting_help) + [HTML manuscript](https://plantbreeding.github.io/BrAPI-Manuscript2/) + [PDF manuscript](https://plantbreeding.github.io/BrAPI-Manuscript2/manuscript.pdf) ++ [MS DOCX manuscript](https://github.com/plantbreeding/BrAPI-Manuscript2/raw/output/manuscript.docx) + [Manubot README](Manubot-README.md) + [Manubot Usage Instructions](USAGE.md) diff --git a/content/01.abstract.md b/content/01.abstract.md index 838c285..df7f936 100644 --- a/content/01.abstract.md +++ b/content/01.abstract.md @@ -1,6 +1,6 @@ ## Abstract {.page_break_before} -Population growth and climate change require extraordinary efforts to increase efficiency in breeding programs around the world. In the last few years, new phenotyping techniques, genomics technologies, and genomic prediction approaches have provided a boost in genetic gain in breeding, but have also created a flood of data that needs careful management to be fully harnessed. Data integration is a significant challenge with multiple types of data being collected and stored by a variety of disparate systems. -The Breeding API (BrAPI) project is an international, grass-roots effort to enable more efficient data management by enabling interoperability among research databases and tools, using a standardized RESTful web service API specification for communicating breeding related data. -This community driven standard is software agnostic and free to be used by anyone interested in breeding data management, including trial, germplasm, phenotypic, and genotyping data management. -This manuscript presents the current version of BrAPI, the substantial growth of the project, and a wide variety of open source breeding research tools with active BrAPI implementations. +Population growth and climate change require extraordinary efforts to increase efficiency in breeding programs around the world. In the last few years, new phenotyping techniques, genomics technologies, and genetic approaches such as genomic prediction have provided a boost in genetic gain in breeding, but have also created a flood of data that needs careful management to be fully harnessed. In particular, data integration is a challenge due to the multiple types of data being handled by a variety of disparate and dispersed systems. +The Breeding API (BrAPI) project is an international, grass-roots effort to enable more efficient data management by enabling interoperability among research databases and tools, using a standardized RESTful web service API specification for exchanging breeding related data. +This community driven standard is software agnostic and free to be used by anyone interested in plant breeding, genetics and agronomy data management, including trial, germplasm, phenotyping, and genotyping data management. +This manuscript presents the substantial growth of the project, a wide variety of open source breeding research tools with active BrAPI implementations, and an overview about the current version of BrAPI. diff --git a/content/02.introduction.md b/content/02.introduction.md index b0971c1..2e8f0e5 100644 --- a/content/02.introduction.md +++ b/content/02.introduction.md @@ -13,30 +13,30 @@ Notes: * Standardization of endpoints across objects - said in a non-tech way --> -To address consequences of climate change and population growth, plant and animal breeding needs to become more efficient and data driven to ensure a healthy, resilient, and sustainable agricultural production system. Modern breeding techniques require large amounts of high quality data to be effective, requiring digital methods for data collection, management, and analysis. Interoperability between breeding software tools, systems, and databases can substantially increase the efficiency of a breeding program. The ability to efficiently share data means access to larger and more complete datasets, enabling more accurate computational models, more accurate predictions, and improved selections. +To address consequences of climate change and population growth, plant and animal breeding needs to become more efficient and data driven to ensure a healthy, resilient, and sustainable agricultural production system. Modern breeding techniques require large amounts of high quality data to be effective, requiring digital methods for data collection, management, and analysis. They rely on several research disciplines, including plant phenomics, genetics, genomics, and agronomy, involving research institutes, genebanks, and breeding companies. Interoperability between research software tools, systems, and databases can substantially increase the efficiency of a breeding program. The ability to efficiently share data means access to larger and more complete datasets, enabling more accurate computational models, more accurate predictions, and improved selections. -The Breeding API (BrAPI) project is an effort to enable interoperability among breeding tools, systems, and databases. BrAPI is a standardized Representational State Transfer (REST), web service, Application Programming Interface (API), specification for breeding and related agricultural data. [@doi:10.1093/bioinformatics/btz190] By using the BrAPI standard, breeding software can more easily become interoperable, allowing groups to more easily share data and software tools. +The Breeding API (BrAPI) project is an effort to enable interoperability among breeding tools, systems, and databases. BrAPI is a standardized Representational State Transfer (REST), web service, Application Programming Interface (API), specification for breeding, genetics, phenomics and related agricultural data. [@doi:10.1093/bioinformatics/btz190] By using the BrAPI standard, breeding software can more easily become interoperable, allowing groups to more easily share data and software tools. -Since its first publication [@doi:10.1093/bioinformatics/btz190], BrAPI has seen a significant increase in community services, compatible tools, and participating organizations. The community has organized numerous hackathons to evolve the specifications, resulting in continuous improvements and enhancements. This report includes a short technical description of the standard and a showcase of the applications, services, and tools available in the BrAPI community. BrAPI has become an essential part of the digital infrastructure for breeding applications and related agricultural projects. It is the intention of this manuscript to demonstrate the value of BrAPI to the wider scientific community as an effective and efficient means to collaborate and share resources. +Since its first publication [@doi:10.1093/bioinformatics/btz190], BrAPI has seen a significant increase in community services, compatible tools, and participating organizations. The community has organized numerous hackathons to evolve the specifications, resulting in continuous improvements and enhancements. This report includes a short technical description of the standard and a showcase of the applications, services, and tools available in the BrAPI community. BrAPI has become an essential part of the digital infrastructure for breeding, genetics and phenomics applications and related agricultural projects. It is the intention of this manuscript to demonstrate the value of BrAPI to the wider scientific community as an effective and efficient means to collaborate and share resources. ### How it works An API is a technical connection between two pieces of software. Just as a Graphical User Interface (GUI) or a Command Line Interface (CLI) allows a human user to interact with a piece of software, an API allows one software application to interact with another. A GUI or CLI might allow a user to input data, read data, and start processes within an application. An API allows one piece of software (sometimes called a client, user agent, or service consumer) to programmatically input data, read data, and start process within another piece of software (sometimes called a server or service provider). -A REST style web service is a type of API commonly used in today's modern web infrastructure. REST is a technical architecture that describes the stateless transmission of data between applications. Typically, RESTful web service APIs are implemented using the standard HTTP protocol that most of the modern internet is built upon. These implementations generally use JavaScript Object Notation (JSON) to represent the data being transferred. Both HTTP and JSON are programming language agnostic, very stable, and very flexible. This means BrAPI can be implemented in almost any piece of software, and can solve a wide range of use cases. +A REST style web service is a type of API commonly used in today's web infrastructure. REST is a technical architecture that describes the stateless transmission of data between applications. Typically, RESTful web service APIs are implemented using the standard HTTP protocol that most of the modern internet is built upon. These implementations generally use JavaScript Object Notation (JSON) to represent the data being transferred. Both HTTP and JSON are programming language agnostic, very stable, and very flexible. This means BrAPI can be implemented in almost any piece of software, and can solve a wide range of use cases. -Data repositories and service providers can choose to represent their data as a BrAPI compatible API. By mapping the internal data structures to the standard models, data repositories can easily expose data to the outside world. Similarly, they can accept new data from external sources and automatically map the new data into an existing database. Client application developers can take advantage of this standardization by building tools and connectors that integrate with all BrAPI compatible data repositories. Visualization, reporting, analytics, data collection, and quality control tools can be built once and shared with other organizations following the standards. As the number of BrAPI compatible databases, tools, and organizations grows, so does the value added by implementing the standard into a given application. +Data repositories and service providers can choose to represent their data as a BrAPI compatible API. By mapping their internal data structures to the standard models, data repositories can easily expose data to the outside world. Similarly, they can accept new data from external sources and automatically map the new data into an existing database. Client application developers can take advantage of this standardization by building tools and connectors that integrate with all BrAPI compatible data repositories. Visualization, reporting, analytics, data collection, and quality control tools can be built once and shared with other organizations following the standards. As the number of BrAPI compatible databases, tools, and organizations grows, so does the value added by implementing the standard into a given application. ### Project Updates -Over its lifetime, the BrAPI project has grown and changed substantially. The latest stable version of the specification (v2.1) looks vastly different from the first version (v1.0) released in 2017. The total size of the specification has almost quadrupled in that time, going from 51 endpoints documented in v1.0 to 201 endpoints documented in v2.1. Because of this growth, the specification documents were reorganized into four modules: BrAPI-Core, BrAPI-Germplasm, BrAPI-Genotyping, and BrAPI-Phenotyping. Figure {@fig:domains} shows a simplified domain map of the whole BrAPI v2.1 data model, divided into the organizational modules. The early versions of the specification focused on read-only phenotype data, with a small consideration to the other domains. Now the specification has a full representation of most of the major concepts applicable to the breeding process. The new specification is also internally consistent, easier to navigate, and allows for read, write, and update capabilities. None of those qualities were a guarantee for the earlier versions. +Over its lifetime, the BrAPI project has grown and changed substantially. The latest stable version of the specification (v2.1) looks vastly different from the first version (v1.0) released in 2017. The total size of the specification has almost quadrupled in that time, going from 51 endpoints documented in v1.0 to 201 endpoints documented in v2.1. Because of this growth, the specification documents were reorganized into four modules: BrAPI-Core, BrAPI-Germplasm, BrAPI-Genotyping, and BrAPI-Phenotyping. Figure {@fig:domains} shows a simplified domain map of the whole BrAPI v2.1 data model, divided into the organizational modules. The early versions of the specification focused on read-only phenotype data, with a small consideration to the other domains. Now the specification has a full representation of most of the major concepts applicable to the breeding and research process. The new specification is also internally consistent, easier to navigate, and allows for read, write, and update capabilities. None of those qualities were a guarantee for the earlier versions. ![A simplified domain map of the whole BrAPI data model, divided into organizational modules. A more detailed Entity Relationship Diagram (ERD) is available on brapi.org.](images/BrAPI_Domains_v2-1_vertical.png){#fig:domains width="100%"} -As the specification has matured, so have the tools, services, and libraries available to the community to work with the specification. Every version of the specification is now released with a change log to guide developers upgrading from a previous version, an Entity Relationship Diagram (ERD) to describe the whole data model visually, and a JSON Schema data model to be used in some automated development efforts. For groups who are using Java, Java Script, Python, R, or Drupal, there are community maintained libraries available that contain full BrAPI implementations ready to be added to some existing code. The BrAPI Test Server and the BRAVA validation tool are both still available to the community for testing purposes, and they have been maintained to support every version of the specification. Finally, there are three new resource list pages on brapi.org to advertise the BrAPI compatible software available in the community. The BrAPPs list page, servers list page, and compatible software list page showcase many of the BrAPI compatible applications and data resources available in the community. +As the specification has matured, so have the tools, services, and libraries available to the community to work with the specification. Every version of the specification is now released with a change log to guide developers upgrading from a previous version, an Entity Relationship Diagram (ERD) to describe the whole data model visually, and a JSON Schema data model to be used in some automated development and validation efforts. For groups who are using Java, Java Script, Python, R, or Drupal, there are community maintained libraries available that contain full BrAPI implementations ready to be added to some existing code. The BrAPI Test Server and the BRAVA validation tool are both still available to the community for testing purposes, and they have been maintained to support every version of the specification. Finally, there are three new resource list pages on brapi.org to advertise the BrAPI compatible software available in the community. The BrAPPs list page, servers list page, and compatible software list page showcase many of the BrAPI compatible applications and data resources available in the community. ### Community Growth The international BrAPI Community consists of software developers, biologists, and other scientists working on BrAPI related projects and data sources. This community is what sustains the BrAPI project, builds implementations, maintains development tools, and provides input to enhance the specification. As the project has grown, so has the community. The BrAPI project started in June 2014 with less than ten people coming together to discuss the idea. Over the next ten years, the community has grown to between 200 and 250 members. -The BrAPI Hackathons are a major staple of the BrAPI community. Twice a year, the community gathers to discuss the specification and collaborate on BrAPI related projects. This time is very valuable to the community; for some organizations, the hackathon is the only time during the year when they have time to work on anything related to BrAPI. During the COVID-19 pandemic, virtual hackathons took the place of in-person events. While the virtual hackathons do not provide the same level of face-to-face time that is crucial to collaborative work, they did allow for more attendees to gather and share their opinions. Going forward, the community leadership has decided to have one in-person hackathon and one virtual hackathon each year, to balance the advantages of both. +The BrAPI Hackathons are a major staple of the BrAPI community. Twice a year, the community gathers to discuss the specification and collaborate on BrAPI related projects. This time is very valuable to the community; for some organizations, the hackathon is the only time during the year when they can collaboratively work on anything related to BrAPI. During the COVID-19 pandemic, virtual hackathons took the place of in-person events. While the virtual hackathons do not provide the same level of face-to-face time that is crucial to collaborative work, they did allow for more attendees to gather and share their opinions. Going forward, the community leadership has decided to have one in-person hackathon and one virtual hackathon each year, to balance the advantages of both. diff --git a/content/03.00.HEADER.Success.md b/content/03.00.HEADER.Success.md index e756780..a2b92d1 100644 --- a/content/03.00.HEADER.Success.md +++ b/content/03.00.HEADER.Success.md @@ -4,3 +4,7 @@ Below are a number of short success stories from the BrAPI community. These tools, applications, and infrastructure projects serve as another indicator of community growth and success over the past 5-10 years. These stories clearly illustrate all the different ways the BrAPI Standard can be used productively and in practice. Figure {@fig:apps} contains a summary of the tools described below. ![A summary of all the tools described below and the general areas each tool is designed to handle](images/BrAPI_Paper_Applications_Chart.png){#fig:apps width="100%"} + + diff --git a/content/03.01.--.HEADER.Phenotyping.md b/content/03.01.--.HEADER.Phenotyping.md index 4a2e8f7..8e78575 100644 --- a/content/03.01.--.HEADER.Phenotyping.md +++ b/content/03.01.--.HEADER.Phenotyping.md @@ -1,4 +1,10 @@ ### Phenotyping - -Phenotyping is fundamental to plant breeding, providing the accurate, high-quality data needed for downstream analyses and decisions. It goes beyond simple data collection, requiring a thorough understanding of research questions and strategic data gathering to ensure successful outcomes. Effective phenotyping can make or break a research project, underscoring the importance of mastering its techniques. The BrAPI specification supports phenotypic data throughout the entire breeding pipeline, from initial collection and standardization to publication and archiving. The community has developed BrAPI-compatible tools to facilitate early data standardization, efficient storage, and curation of phenotypic data and trait metadata. Additionally, there are ongoing efforts to create tools for managing images and other high-throughput phenotyping techniques, further enhancing the precision and efficiency of plant breeding research. + +Phenotyping is fundamental to plant breeding and genetics research, providing the accurate, high-quality data needed for downstream analyses and selection decisions. +Effective phenotyping requires a thorough understanding of both biological research questions and operational data gathering to ensure successful outcomes. +Collected data, subsequent analyses, and data visualizations all impact and shape downstream experiments. +The BrAPI specification supports phenotypic data throughout the entire breeding pipeline, including collection, analyses, publication, and archiving. +The BrAPI community has developed several BrAPI-compatible tools to facilitate data standardization, efficient storage, and curation of phenotypic data and trait metadata. +Ongoing development efforts are creating tools to manage images and other high-throughput phenotypic data sources, further enhancing the precision and efficiency of plant breeding research. +By supporting the accurate and efficient collection and storage of phenotypic data, BrAPI compatible tools simplify the conversion of phenotypes into insights that are necessary to help digitize and boost modern breeding and genetics research programs. diff --git a/content/03.01.01.Field_Book.md b/content/03.01.01.Field_Book.md index 3ebb045..87c6e1a 100644 --- a/content/03.01.01.Field_Book.md +++ b/content/03.01.01.Field_Book.md @@ -1,8 +1,9 @@ #### Field Book -Phenotypic data collection underpins scientific crop research and plant breeding. Knowledge gained from collected data and its analysis, alongside data visualizations, inform further phenotypic trials and ideally support research hypotheses. The importance of accuracy and efficiency in the collection of this data as well as the infrastructure to facilitate the flow of data from the field to a knowledge base cannot be underestimated. -[Field Book](fieldbook.phenoapps.org/) [@doi:10.2135/cropsci2013.08.0579] is an android based mobile app for collecting field data. Historically, gathering data in the field was done with pen and paper, or perhaps some version of a digital spreadsheet. The abundance and prevalence of smart phones has allowed Field Book to enhance data collection. Field Book can create well-formed digital observation records from the moment they are taken. This can improve the efficiency of data collection and reduce human error. - -In 2018, BrAPI was introduced into Field Book, allowing for the automated the flow of data from the mobile app into a central, BrAPI compatible, database server. This workflow allows data collection and storage to be expedited, removing the need of the user to export and transfer data files manually. Since Field Book’s adoption of BrAPI, many community servers have been integrated to simplify data storage. In this work flow, data is collected and stored completely digitally with little-to-no human involvement. +Data from plant breeding and genetics experiments has traditionally been collected using pen and paper, but this approach often results in transcription errors and delayed analysis. +[Field Book](fieldbook.phenoapps.org/) [@doi:10.2135/cropsci2013.08.0579], a highly-customizable Android app, was developed to help scientists digitize and organize their phenotypic data as measurements are collected. +This effectively improves data collection speed, reduces errors, and enables larger and more robust breeding populations and data sets. +Field Book has added support for BrAPI to streamline data transfer to and from BrAPI-compatible servers. +Removing the need to manually transfer data files simplifies data exchange between these systems and reduces the opportunities for human error and data loss. diff --git a/content/03.01.04.Image_Breed.md b/content/03.01.04.Image_Breed.md index 479b4a4..bacdd09 100644 --- a/content/03.01.04.Image_Breed.md +++ b/content/03.01.04.Image_Breed.md @@ -1,5 +1,5 @@ #### ImageBreed -[ImageBreed](https://imagebreed.org/) [@doi:10.1002/ppj2.20004] is an image collection pipeline tool to support regular use of UAVs and UGVs. High-throughput phenotyping has been gaining significant traction lately as a way to collect lots of data very quickly. Image collection from unmanned aerial and ground vehicles (UAVs and UGVs) are a great way to collect a lot of raw data all at once, then analyze it later. +[ImageBreed](https://imagebreed.org/) [@doi:10.1002/ppj2.20004] is an image collection pipeline tool to support regular use of UAVs and UGVs. High-throughput phenotyping has been gaining significant traction lately as a way to collect large amounts of data very quickly. Image collection from unmanned aerial and ground vehicles (UAVs and UGVs) are a great way to collect a lot of raw data all at once, then analyze it later. When the raw images have been processed through the standardization pipelines in ImageBreed, useful phenotypes can be extracted from the images. The BrAPI standard is used to push these phenotypes back to a central breeding database where they can be analyzed with other data. In addition to this, ImageBreed has the ability to use BrAPI to upload the raw images to the central breeding database, or any other BrAPI compatible long term storage service. In the current version of the standard (V2.1), the BrAPI data models for images are rudimentary, but effective. The ImageBreed team has put in some work into enhancing the BrAPI image data standards, collaborating with others in the community. diff --git a/content/03.01.05.PHIS.md b/content/03.01.05.PHIS.md index 524933e..0156269 100644 --- a/content/03.01.05.PHIS.md +++ b/content/03.01.05.PHIS.md @@ -3,6 +3,6 @@ [PHIS](http://www.phis.inrae.fr/) [@doi:10.1111/nph.15385], the Hybrid Phenotyping Information System, is an ontology-driven information system based on semantic web technologies, based on the [OpenSILEX](https://github.com/OpenSILEX/) framework. PHIS is deployed in several field and greenhouse platforms of the French national [PHENOME](https://www.phenome-emphasis.fr/) and European [EMPHASIS](https://emphasis.plant-phenotyping.eu/) infrastructures. It manages and collects data from basic phenotyping and high throughput phenotyping experiments on a day to day basis. PHIS unambiguously identifies all the objects and traits in an experiment, and establishes their types and relationships via ontologies and semantics. -PHIS has been designed to be BrAPI-compliant. PHIS adheres to the standards and protocols specified by BrAPI and implements various services aligning with the BrAPI standards, encompassing the Core, Phenotyping, and Germplasm modules. This enables integration and compatibility with BrAPI-compliant systems and platforms, such as OLGA, a genebank accessions management system, to retrieve accession information. This prerequisite served as the basis for formalizing the data model, while also facilitating compatibility with other standards, such as the Minimal Information About a Plant Phenotyping Experiment ([MIAPPE](https://www.miappe.org/) [@doi:10.1111/nph.16544]). By integrating BrAPI requirements into its structure, PHIS not only meets the standards of the phenotyping field, but also strengthens its capacity for interoperability and effective collaboration in the wider context of plant breeding and related fields. The fact that data within a PHIS instance can be queried through BrAPI services enables indexing of PHIS in [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) [@https://urgi.versailles.inrae.fr/faidare]. +PHIS has been designed to be BrAPI-compliant. PHIS adheres to the standards and protocols specified by BrAPI and implements various services aligning with the BrAPI standards, encompassing the Core, Phenotyping, and Germplasm modules. This enables integration and compatibility with BrAPI-compliant systems and platforms, such as OLGA, a genebank accessions management system, to retrieve accession information. This prerequisite served as the basis for formalizing the data model, while also facilitating compatibility with other standards, such as the Minimal Information About a Plant Phenotyping Experiment ([MIAPPE](https://www.miappe.org/) [@doi:10.1111/nph.16544]). By integrating BrAPI requirements into its structure, PHIS not only meets the standards of the phenotyping field, but also strengthens its capacity for interoperability and effective collaboration in the wider context of plant breeding and related fields. The fact that data within a PHIS instance can be queried through BrAPI services enables indexing of PHIS in the [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) data portal [@https://hal.inrae.fr/hal-04425516]. Furthermore, as PHIS offers BrAPI-compliant Web Services, it simplifies the integration and data exchange with other European information systems that handle phenotyping data. The adhesion to BrAPI standards ensures a common interface and compatibility, facilitating communication and collaboration between PHIS and other systems in the European context. This interoperability not only eases data sharing, but also promotes a more coherent and efficient approach to the management and use of phenotyping data on various platforms and research initiatives within the European scientific community. diff --git a/content/03.01.06.PIPPA.md b/content/03.01.06.PIPPA.md index f02bbe0..65eb3e8 100644 --- a/content/03.01.06.PIPPA.md +++ b/content/03.01.06.PIPPA.md @@ -3,6 +3,6 @@ [PIPPA](https://pippa.psb.ugent.be) [@https://pippa.psb.ugent.be] is a data management system used for collecting data from the [WIWAM](https://www.wiwam.be/) [@https://www.wiwam.be] range of automated high throughput phenotyping platforms. These platforms have been deployed at different research institutes and commercial breeders across Europe. They can be setup in a variety of configurations with different types of equipment including weighing scales, cameras, and environment sensors. The software features a web interface with functionality for setting up new experiments, planning imaging and irrigation treatments, linking metadata to pots (genotype, growth media, manual treatments), importing data, exporting data, and visualizing data. It also supports the integration of image analysis scripts and connections to a compute cluster for job submission. -To share the phenotype data of the experiments linked to publications, an implementation of BrAPI v1.3 was developed which allowed read only access to the data in the BrAPI standardized format. This server was registered on [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) [@https://urgi.versailles.inrae.fr/faidare] which allows the data to be found alongside data from other BrAPI compatible repositories. +To share the phenotype data of the experiments linked to publications, an implementation of BrAPI v1.3 was developed which allowed read only access to the data in the BrAPI standardized format. This server was registered on [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) [@https://hal.inrae.fr/hal-04425516] which allows the data to be found alongside data from other BrAPI compatible repositories. -As the BrAPI ecosystem has matured, it has created a clear path for the further development of PIPPA. THe BrAPI specification demonstrates how to share data in a manner consistent with the FAIR principles, [@doi:10.1038/sdata.2016.18] which are becoming best practices in plant research data management. The BrAPI technical standard, in combination with the [MIAPPE](https://www.miappe.org/) [@doi:10.1111/nph.16544] scientific standard, have served as guidelines in the current development effort of the PIPPA project. This development is focused on delivering a public BrAPI v2.1 endpoint and making more high throughput datasets publicly available via BrAPI. +As the BrAPI ecosystem has matured, it has created a clear path for the further development of PIPPA. The BrAPI specification demonstrates how to share data in a manner consistent with the FAIR principles, [@doi:10.1038/sdata.2016.18] which are becoming best practices in plant research data management. The BrAPI technical standard, in combination with the [MIAPPE](https://www.miappe.org/) [@doi:10.1111/nph.16544] scientific standard, have served as guidelines in the current development effort of the PIPPA project. This development is focused on delivering a public BrAPI v2.1 endpoint and making more high throughput datasets publicly available via BrAPI. diff --git a/content/03.02.06.DArT.md b/content/03.02.01.DArT.md similarity index 100% rename from content/03.02.06.DArT.md rename to content/03.02.01.DArT.md diff --git a/content/03.02.01.Flapjack.md b/content/03.02.04.Flapjack.md similarity index 100% rename from content/03.02.01.Flapjack.md rename to content/03.02.04.Flapjack.md diff --git a/content/03.02.04.GIGWA.md b/content/03.02.05.GIGWA.md similarity index 100% rename from content/03.02.04.GIGWA.md rename to content/03.02.05.GIGWA.md diff --git a/content/03.02.05.PHG.md b/content/03.02.06.PHG.md similarity index 100% rename from content/03.02.05.PHG.md rename to content/03.02.06.PHG.md diff --git a/content/03.03.05.FLORILEGE.md b/content/03.03.02.FLORILEGE.md similarity index 100% rename from content/03.03.05.FLORILEGE.md rename to content/03.03.02.FLORILEGE.md diff --git a/content/03.03.04.GLIS.md b/content/03.03.03.GLIS.md similarity index 100% rename from content/03.03.04.GLIS.md rename to content/03.03.03.GLIS.md diff --git a/content/03.03.03.Helium.md b/content/03.03.04.Helium.md similarity index 100% rename from content/03.03.03.Helium.md rename to content/03.03.04.Helium.md diff --git a/content/03.03.02.MGIS.md b/content/03.03.05.MGIS.md similarity index 92% rename from content/03.03.02.MGIS.md rename to content/03.03.05.MGIS.md index eeca511..508c09a 100644 --- a/content/03.03.02.MGIS.md +++ b/content/03.03.05.MGIS.md @@ -1,6 +1,6 @@ #### MGIS -The Musa Germplasm Information System ([MGIS](https://www.crop-diversity.org/mgis/)) serves as a comprehensive community portal dedicated to banana diversity, a crop critical to global food security [@doi:10.1093/database/bax046]. MGIS offers detailed information on banana germplasm, focusing on the collections held by the CGIAR International Banana Genebank (ITC) [@doi:10.1186/s43170-020-00015-6]. It is built on the Drupal/Tripal technology, like BIMS [@doi:10.1093/database/baab054] and Florilège. +The [Musa Germplasm Information System (MGIS)](https://www.crop-diversity.org/mgis/) serves as a comprehensive community portal dedicated to banana diversity, a crop critical to global food security [@doi:10.1093/database/bax046]. MGIS offers detailed information on banana germplasm, focusing on the collections held by the CGIAR International Banana Genebank (ITC) [@doi:10.1186/s43170-020-00015-6]. It is built on the Drupal/Tripal technology, like BIMS [@doi:10.1093/database/baab054] and Florilège. Since its inception, MGIS developers have actively participated in the BrAPI community. The MGIS team pushed for the integration of the Multi-Crop Passport Data (MCPD) standard into the Germplasm module of the API. MCPD support was added in BrAPI v1.3, and MGIS now provides passport data information on ITC banana genebank accessions (with GLIS DOI), synchronized with [Genesys](https://www.genesys-pgr.org/a/overview/v2YdWZGrZjD). MGIS also enriches the passport data by incorporating additional information from other germplasm collections worldwide. All the germplasm data is available through the BrAPI Germplasm module implementation. For genotyping data, MGIS integrates with Gigwa [@doi:10.1093/gigascience/giz051], which provides a tailored implementation of the BrAPI genotyping module. Furthermore, MGIS supports a set of BrAPI phenotyping endpoints, facilitating the exposure of morphological descriptors and trait information supported by ontologies like the Crop Ontology [@doi:10.1093/aobpla/plq008]. MGIS has integrated the Trait Selector BrAPP, and there are use cases implemented to interlink genebank and breeding data between MGIS and the breeding database MusaBase. diff --git a/content/03.03.06.FAIDARE.md b/content/03.03.06.FAIDARE.md deleted file mode 100644 index 266a476..0000000 --- a/content/03.03.06.FAIDARE.md +++ /dev/null @@ -1,10 +0,0 @@ -#### FAIDARE - - -[FAIDARE](https://urgi.versailles.inrae.fr/faidare/) [@https://urgi.versailles.inrae.fr/faidare] is a data discovery portal providing a biologist friendly search system over a global federation of 33 plant research databases. It allows a user to identify data resources using a full text search approach combined with domain specific filters. Each search result contains a link back to the original database for visualization, analysis, and download. The indexed data types are very broad and include genomic features, selected bibliography, QTL, markers, genetic variation studies, phenomic studies, and plant genetic resources. This inclusiveness is achieved thanks to a two stage indexation data model. The first, most generic, index provides basic search functionalities and relies on five fields: name, link back URL, data type, species, and exhaustive description. To provide more advanced filtering, the second stage indexation mechanism takes advantage of BrAPI endpoints to get more detailed metadata on genotyping and phenotyping studies. - -The indexation mechanism relies on a [public software package](https://github.com/elixir-europe/plant-brapi-etl-faidare) that allows data resource managers to request the indexation of their database. This BrAPI client is able to extract data from any BrAPI 1.3 and 1.2 endpoint. The development of BrAPI 2.x indexation will be initiated in 2025. Since not all databases are willing to implement BrAPI endpoints, it is possible to generate metadata as static, BrAPI compliant, json files, using the standard as a file exchange format. - -The FAIDARE architecture has been designed by elaborating on the GnpIS Software Architecture [@doi:10.34133/2019/1671403]. As a consequence, BrAPI is at the core of its data model. The JSON data files served by the Elasticsearch NoSQL engine are enriched versions of the BrAPI JSON data model. FAIDARE also includes a BrAPI endpoint that serves all indexed metadata. FAIDARE has been adopted by several communities including the ELIXIR and EMPHASIS european infrastructures, and the WheatIS of the Wheat-Initiative. Several databases are added each year to the FAIDARE global federation, allowing to increase both the portal and the BrAPI adoption. - - diff --git a/content/03.04.--.HEADER.Data_Management.md b/content/03.04.--.HEADER.Data_Management.md index df1984c..0563106 100644 --- a/content/03.04.--.HEADER.Data_Management.md +++ b/content/03.04.--.HEADER.Data_Management.md @@ -1,3 +1,3 @@ -### Breeding Data Management +### Breeding and Genetics Data Management -While specialty data management is important for some use cases, often breeders want a central repository of critical data. General breeding data management systems support some level of phenotypic, genotypic, and germplasm data, as well as trial, equipment, and people management. By enabling BrAPI support, these larger systems can connect with smaller tools and specialty systems to provide more functionality under the same user interface. There are several breeding data management systems developed in the BrAPI community, each with their own strengths. +While specialty data management is important for some use cases, often breeders want a central repository or access point of critical data. General breeding and genetics data management systems and web portals support some level of phenotypic, genotypic, and germplasm data, as well as trial, equipment, and people management. By enabling BrAPI support, these larger systems can connect with smaller tools and specialty systems to provide more functionality under the same user interface. There are several breeding data management systems developed in the BrAPI community, each with their own strengths. diff --git a/content/03.04.01.BIMS.md b/content/03.04.01.BIMS.md new file mode 100644 index 0000000..529d7d3 --- /dev/null +++ b/content/03.04.01.BIMS.md @@ -0,0 +1,6 @@ +#### BIMS + + +The [Breeding Information Management System (BIMS)](https://wwww.breedwithbims.org) [@doi:10.1093/database/baab054] is a free, secure, and online breeding management system which allows breeders to store, manage, archive, and analyze their private breeding program data. BIMS enables individual breeders to have complete control of their own breeding data along with access to tools such as data import/export, data analysis, and data archiving for their germplasm, phenotype, genotype, and image data. BIMS is currently implemented in five community databases, the Genome Database for Rosaceae [@doi:10.1093/nar/gky1000], CottonGEN [@doi:10.3390/plants10122805], the Citrus Genome Database, the Pulse Crop Database, and the Genome Database for Vaccinium. BIMS in these five community databases enables individual breeders to import publicly available data so that they can utilize public data in their breeding program. BIMS is also implemented in the public database [breedwithbims.org](https://wwww.breedwithbims.org) that any crop breeder can use. + +Right now, BIMS primarily utilizes BrAPI to connect with the Field Book Android App [@doi:10.2135/cropsci2013.08.0579], enabling seamless data transfer between BIMS and the app. Data transfer through BrAPI between BIMS and other resources such as BreedBase[@doi:10.1093/g3journal/jkac078], GIGWA[@doi:10.1093/gigascience/giz051], and Breeder Genomics Hub is on the way. Hopefully, the BIMS development team can easily reuse some of the solved use cases and workflows created by others in the BrAPI community. diff --git a/content/03.04.04.BIMS.md b/content/03.04.04.BIMS.md deleted file mode 100644 index 4172423..0000000 --- a/content/03.04.04.BIMS.md +++ /dev/null @@ -1,6 +0,0 @@ -#### BIMS - - -[BIMS](https://wwww.breedwithbims.org) (Breeding Information Management System) [@doi:10.1093/database/baab054] is a free, secure, and online breeding management system which allows breeders to store, manage, archive, and analyze their private breeding program data. BIMS enables individual breeders to have complete control of their own breeding data along with access to tools such as data import/export, data analysis, and data archiving for their germplasm, phenotype, genotype, and image data. BIMS is currently implemented in five community databases, the Genome Database for Rosaceae [@doi:10.1093/nar/gky1000], CottonGEN [@doi:10.3390/plants10122805], the Citrus Genome Database, the Pulse Crop Database, and the Genome Database for Vaccinium. BIMS in these five community databases enables individual breeders to import publicly available data so that they can utilize public data in their breeding program. BIMS is also implemented in the public database [breedwithbims.org](https://wwww.breedwithbims.org) that any crop breeder can use. - -Right now, BIMS primarily utilizes BrAPI to connect with the Field Book Android App [@doi:10.2135/cropsci2013.08.0579], enabling seamless data transfer between BIMS and the app. Data transfer through BrAPI between BIMS and other resources such as BreedBase[@doi:10.1093/g3journal/jkac078], GIGWA[@doi:10.1093/gigascience/giz051], and Breeder Genomics Hub is on the way. Hopefully, the BIMS development team can easily reuse some of the solved use cases and workflows created by others in the BrAPI community. diff --git a/content/03.04.01.DeltaBreed.md b/content/03.04.04.DeltaBreed.md similarity index 72% rename from content/03.04.01.DeltaBreed.md rename to content/03.04.04.DeltaBreed.md index b420652..635e533 100644 --- a/content/03.04.01.DeltaBreed.md +++ b/content/03.04.04.DeltaBreed.md @@ -5,7 +5,5 @@ DeltaBreed users need not be aware of BrAPI or the specifics of underlying applications but will notice that BrAPI interoperability reduces the need for human-mediated file transfers and data manipulation. Field Book users, for example, can connect to their DeltaBreed program, authenticate, and pull studies and traits directly from DeltaBreed to Field Book on their data collection device. The subsequent step of pushing observations from Field Book to DeltaBreed is straightforward via BrAPI, but is pending implementation until observation transaction handling is improved, intentional and inadvertent repeated measures are differentiated, and a data staging area is implemented for quality control. -Breeding Insight integrated several BrAPI applications to support 2021 phenotypic data collection by USDA-ARS blueberry breeders. DeltaBreed was used to create traits in Breedbase, and Field Book was used to pull studies and traits from Breedbase. The workflow also permitted users to push Field Book observations back to Breedbase via BrAPI. This effort served as a successful proof of concept for multi-application BrAPI integration, but highlighted limitations to the process of accepting BrAPI observations from Field Book. The Breeding Insight team is actively working with the rest of the BrAPI community to correct these limitations in future versions of the specification. - -DeltaBreed is integrated with many other BrAPI community projects and tools. There is a BrAPI enabled connection, either in development or production, with all of the following tools: [BrAPI Java Test Server](https://test-server.brapi.org/brapi/v2/), [BreedBase](https://breedbase.org/), [Field Book](https://play.google.com/store/apps/details?id=com.fieldbook.tracker), [Gigwa](https://gigwa.southgreen.fr/gigwa/), [QBMS](https://icarda-git.github.io/QBMS), [Mr Bean](https://github.com/AparicioJohan/MrBeanApp), [Helium](https://helium.hutton.ac.uk/#/) and the [Pedigree Viewer](https://github.com/solgenomics/BrAPI-Pedigree-Viewer) BrAPP. +DeltaBreed is integrated with at least eight other BrAPI community tools, and plans to integrate with several more as the project continues. There is a BrAPI enabled connection, either in development or production, with all of the following tools: [BrAPI Java Test Server](https://test-server.brapi.org/brapi/v2/), [BreedBase](https://breedbase.org/), [Field Book](https://play.google.com/store/apps/details?id=com.fieldbook.tracker), [Gigwa](https://gigwa.southgreen.fr/gigwa/), [QBMS](https://icarda-git.github.io/QBMS), [Mr Bean](https://github.com/AparicioJohan/MrBeanApp), [Helium](https://helium.hutton.ac.uk/#/) and the [Pedigree Viewer](https://github.com/solgenomics/BrAPI-Pedigree-Viewer) BrAPP. diff --git a/content/03.04.05.FAIDARE.md b/content/03.04.05.FAIDARE.md new file mode 100644 index 0000000..4633709 --- /dev/null +++ b/content/03.04.05.FAIDARE.md @@ -0,0 +1,10 @@ +#### FAIDARE + + +[FAIDARE](https://urgi.versailles.inrae.fr/faidare/) [@https://hal.inrae.fr/hal-04425516] is a data discovery portal providing a biologist friendly search system over a global federation of 40 plant research databases at the time of writing. It allows users to identify data resources using a full text search approach combined with domain specific filters. Each search result contains a link back to the original database for visualization, analysis, and download. The indexed data types are very broad and include genomic features, selected bibliography, QTL, markers, genetic variation studies, phenomic studies, and plant genetic resources. This inclusiveness is achieved thanks to a two stage indexation data model. The first index, more generic, provides basic search functionalities and relies on five fields: name, link back URL, data type, species, and exhaustive description. To provide more advanced filtering, the second stage indexation mechanism takes advantage of BrAPI endpoints to get more detailed metadata on germplasm, genotyping studies and phenotyping studies. + +The indexation mechanism relies on a [public software package](https://github.com/elixir-europe/plant-brapi-etl-faidare) that allows data resource managers to request the indexation of their database. This BrAPI client is able to extract data from any BrAPI 1.3 and 1.2 endpoint. The development of BrAPI 2.x indexation will be initiated in 2025. Since not all databases are willing to implement BrAPI endpoints, it is possible to generate metadata as static BrAPI compliant JSON files, using the BrAPI standard as a file exchange format. + +The FAIDARE architecture has been designed by elaborating on the BrAPI data model mixed with the GnpIS Software Architecture [@doi:10.34133/2019/1671403]. It uses an Elasticsearch NoSQL engine that searches and serves enriched versions of the BrAPI JSON data model. FAIDARE also includes a BrAPI endpoint using all indexed metadata. It has been adopted by several communities including the ELIXIR and EMPHASIS European infrastructures, and the WheatIS of the Wheat-Initiative. Several databases are added each year to the FAIDARE global federation, allowing to increase both the portal and the BrAPI adoption. + + diff --git a/content/03.04.05.Germinate.md b/content/03.04.06.Germinate.md similarity index 58% rename from content/03.04.05.Germinate.md rename to content/03.04.06.Germinate.md index 0e3777d..57d3325 100644 --- a/content/03.04.05.Germinate.md +++ b/content/03.04.06.Germinate.md @@ -1,6 +1,6 @@ #### Germinate -[Germinate](https://ics.hutton.ac.uk/get-germinate/) [@doi:10.1002/csc2.20248] is an open-source plant genetic resources database that combines and integrates various kinds of plant breeding data including genotypic data, phenotypic trials data, passport data, images, geographic information and climate data into a single repository. Germinate is tightly linked to the BrAPI specification and supports a majority of BrAPI endpoints for querying, filtering, and submission. +[Germinate](https://ics.hutton.ac.uk/get-germinate/) [@doi:10.1002/csc2.20248;@doi:10.2135/cropsci2016.09.0814] is an open-source plant genetic resources database that combines and integrates various kinds of plant breeding data including genotypic data, phenotypic trials data, passport data, images, geographic information and climate data into a single repository. Germinate is tightly linked to the BrAPI specification and supports a majority of BrAPI endpoints for querying, filtering, and submission. Germinate integrates and connects with other BrAPI-enabled tools such as GridScore for phenotypic data collection, Flapjack for genotypic data visualization, and Helium for pedigree visualization. Additionally, due to the nature of BrAPI, Germinate can act as a data repository for any BrAPI-compatible tool. Thanks to the interoperability provided by BrAPI, the need for manual data handling becomes a rarity with the direct benefit of faster data processing, fewer to no human errors, data security, and data integrity. diff --git a/content/03.05.--.HEADER.Analytics.md b/content/03.05.--.HEADER.Analytics.md index 3554dd1..f0e81ba 100644 --- a/content/03.05.--.HEADER.Analytics.md +++ b/content/03.05.--.HEADER.Analytics.md @@ -1,3 +1,5 @@ ### Analytics -While other tools listed above have the capability to do specialized analytics on certain types of data, general analytics tools can cover a wide range of data types and analytical models. The tools developed by the BrAPI community can pull in data from multiple BrAPI compatible data sources and provide enhanced analytical functionality. In many cases, there is no longer a need to import and export large data files to a local computational environment just to run standard analytical models. These tools are able to extract the data they need from a data source without much human intervention or human error. +Modern breeding programs can utilize data management systems to maintain both phenotypic and genotypic data. Numerous systems are available for adoption. To fully leverage the benefits of digitalization in this ecosystem, breeders need to utilize data from different sources to make efficient data-driven decisions. With increased computational power at their disposal, scientists can construct more advanced analysis pipelines by combining various data sources. + +The tools developed by the BrAPI community can pull in data from multiple BrAPI compatible data sources and provide enhanced analytical functionality. In many cases, there is no longer a need to import and export large data files to a local computational environment just to run standard analytical models. These tools are able to extract the data they need from a data source without much human intervention or human error. diff --git a/content/03.05.01.G-Crunch.md b/content/03.05.01.G-Crunch.md new file mode 100644 index 0000000..83c3e64 --- /dev/null +++ b/content/03.05.01.G-Crunch.md @@ -0,0 +1,6 @@ +#### G-Crunch + + +G-Crunch is an upcoming user-facing analysis tool that integrates genomic and phenotypic data to fulfill the need for simple, user driven analytics requests. It includes a generic user interface and the ability to swap out data sources and analysis tools. The G-Crunch team hopes to streamline repeatable, debuggable, simple analytic requests and results. + +G-Crunch, as a tool, couldn't feasibly exist without BrAPI. The support of BrAPI interfaces allows G-Crunch to use one unified request method, and adapt to the user's (BrAPI-compliant) existing network of tools. This lowers the barrier to entry for adoption, and makes analysis pipelines easily repeatable. diff --git a/content/03.05.01.QBMS.md b/content/03.05.02.QBMS.md similarity index 74% rename from content/03.05.01.QBMS.md rename to content/03.05.02.QBMS.md index d5e2fb6..4566968 100644 --- a/content/03.05.01.QBMS.md +++ b/content/03.05.02.QBMS.md @@ -1,7 +1,6 @@ #### QBMS -Modern breeding programs can utilize data management systems to maintain both phenotypic and genotypic data. Numerous systems are available for adoption. To fully leverage the benefits of digitalization in this ecosystem, breeders need to utilize data from different sources to make efficient data-driven decisions. With increased computational power at their disposal, scientists can construct more advanced analysis pipelines by combining various data sources. The [QBMS](https://icarda-git.github.io/QBMS) [@doi:10.5281/zenodo.10791627] R package eliminates technical barriers scientists experience when using the BrAPI specification in their analysis scripts and pipelines. This barrier arises from the complexity of managing API backend processes, such as authentication, tokens, TCP/IP protocol, JSON format, pagination, stateless calls, asynchronous communication, database IDs, and more. To bridge this gap, the QBMS package abstracts the technical complexities, providing breeders with stateful functions familiar to them when navigating their GUI systems. It enables them to query and extract data into a standard data frame structure, consistent with their use of the R language, one of the most common statistical tools in the breeding community. diff --git a/content/03.05.03.G-Crunch.md b/content/03.05.03.G-Crunch.md deleted file mode 100644 index d867146..0000000 --- a/content/03.05.03.G-Crunch.md +++ /dev/null @@ -1,6 +0,0 @@ -#### G-Crunch - - -G-Crunch is an upcoming user-facing analysis tool that attempts to fill the space of simple, user driven analytics requests, with a generic user interface and the ability to swap out data sources and analysis tools. G-Crunch hopes to streamline repeatable, debuggable, simple analytic requests and results. - -G-Crunch, as a tool, couldn't feasibly exist without BrAPI. The support of BrAPI interfaces allows G-Crunch to use one unified request method, and adapt to the user's (BrAPI-compliant) existing network of tools. This lowers the barrier to entry for adoption, and makes analysis pipelines easily repeatable. diff --git a/content/03.05.02.Mr_Bean.md b/content/03.05.03.Mr_Bean.md similarity index 100% rename from content/03.05.02.Mr_Bean.md rename to content/03.05.03.Mr_Bean.md diff --git a/content/03.05.05.SCT.md b/content/03.05.04.SCT.md similarity index 51% rename from content/03.05.05.SCT.md rename to content/03.05.04.SCT.md index b0d6d8f..feef13d 100644 --- a/content/03.05.05.SCT.md +++ b/content/03.05.04.SCT.md @@ -1,7 +1,7 @@ #### SCT -The [Sugarcane Crossing Tool](https://github.com/USDA-ARS-GBRU/SugarcaneCrossingTool) (SCT) is a lightweight RShiny dashboard application designed to receive, process, and visualize data from a linked BreedBase [@doi:10.1093/g3journal/jkac078] instance. This application is being developed collaboratively with members of the [Sugarcane Integrated Breeding System](https://www.amscl.org/sugarcane-integrated-breeding-system/), who have advocated for an application that assists them in designing crosses based on queried information from a list of available accessions. By leveraging existing community resources, the team has been able to develop a simple, BrAPI-enabled, application without possessing extensive programming knowledge or experience. The SCT is presented as an inspiration for similarly positioned scientists to consider developing custom applications for specific tasks. +The [Sugarcane Crossing Tool (SCT)](https://github.com/USDA-ARS-GBRU/SugarcaneCrossingTool) is a lightweight R-Shiny dashboard application designed to receive, process, and visualize data from a linked BreedBase [@doi:10.1093/g3journal/jkac078] instance. This application is being developed collaboratively with members of the [Sugarcane Integrated Breeding System](https://www.amscl.org/sugarcane-integrated-breeding-system/), who have advocated for an application that assists them in designing crosses based on queried information from a list of available accessions. By leveraging existing community resources, the team has been able to develop a simple, BrAPI-enabled, application without possessing extensive programming knowledge or experience. The SCT is presented as an inspiration for similarly positioned scientists to consider developing custom applications for specific tasks. diff --git a/content/03.05.04.ShinyBrAPPs.md b/content/03.05.04.ShinyBrAPPs.md deleted file mode 100644 index 8747e8b..0000000 --- a/content/03.05.04.ShinyBrAPPs.md +++ /dev/null @@ -1,5 +0,0 @@ -#### ShinyBrAPPs - -Data management systems are generic and robust centralized applications, with a large community of users, long term development cycles and release plans. BrAPI compliance offers these systems the opportunity to add functionalities in a modular way through the development of external plugin applications that can quickly fulfill specific needs of a group of users. [R-Shiny](https://shiny.posit.co/) is a R-package that opens the possibility to develop rich and productive interactive web applications to R users and developers. As such it allows user communities to quickly prototype and produce applications that are finely tailored to their needs, thus improving adoption and daily use of data management systems. The Breeding Management System (BMS) of the IBP and Gigwa are BrAPI compliant and are being widely used by breeding programs, including national breeding programs in Africa. CIRAD and the IBP teams have been working together as part of the [IAVAO](https://www.iavao.org/) breeders community to develop the Shiny-BrAPPs, a set of R-Shiny applications using BrAPI as a core technology for data exchange. These applications are connected to BMS and/or Gigwa and provide tools for specific use cases. So far, four applications have been developed covering the fields of trial data quality control, single trial statistical analysis, breeding decision support, and raw genotyping data visual inspection. - -The "BMS trial data explorer" retrieves data from a single multi-location trial and displays data counts and summary boxplot for all variables measured in different studies. It also provides an interactive distribution plot to easily select observations that require curation and a report of candidate issues that needs to be addressed by the breeder. The "STABrAPP" tool is an application for single trial mixed model analysis. It basically provides a GUI to the [StatGen-STA](https://biometris.github.io/statgenSTA/) R package. The "DSBrAPP" tool is a decision support tool helping breeders to select germplasm according to their various characteristics and save this germplasm list into BMS. These first three apps are grouped into a single repository ([ShinyBrAPPs](https://github.com/IntegratedBreedingPlatform/ShinyBrAPPs/)) with modular code shared across applications and make use of the [brapir-v1](https://github.com/mverouden/brapir-v1) and [brapir-v2](https://github.com/mverouden/brapir-v2) R packages. Finally, the "[snpclust](https://github.com/jframi/snpclust)" tool enables a user to check and manually correct the clustering of fluorescence based SNP genotyping data. diff --git a/content/03.05.05.ShinyBrAPPs.md b/content/03.05.05.ShinyBrAPPs.md new file mode 100644 index 0000000..ded77fe --- /dev/null +++ b/content/03.05.05.ShinyBrAPPs.md @@ -0,0 +1,5 @@ +#### ShinyBrAPPs + +The [ShinyBrAPPs](https://github.com/IntegratedBreedingPlatform/ShinyBrAPPs/) code repository contains a number of useful tools, built using the[R-Shiny](https://shiny.posit.co/) framework and the [BrAPI R](https://github.com/mverouden/brapir-v2) open source library. The R-Shiny framework allows user communities to quickly prototype and produce applications that are finely tailored to their needs, thus improving adoption and daily use of data management systems. An international collaboration of developers from CIRAD and the IBP have been working together as part of the [IAVAO](https://www.iavao.org/) breeders community to develop these ShinyBrAPPs, in support of national breeding programs in western Africa. These applications are typically connected to BMS and/or Gigwa and provide tools for specific use cases. BrAPI compliance offers these systems the opportunity to add functionalities in a modular way through the development of external plugin applications that can quickly fulfill specific needs for this group of breeders and scientists. + +So far, four applications have been developed covering the fields of trial data quality control, single trial statistical analysis, breeding decision support, and raw genotyping data visual inspection. The "BMS trial data explorer" retrieves data from a single multi-location trial and displays data counts and summary box-plot for all variables measured in different studies. It also provides an interactive distribution plot to easily select observations that require curation and a report of candidate issues that needs to be addressed by the breeder. The "STABrAPP" tool is an application for single trial mixed model analysis. It basically provides a GUI to the [StatGen-STA](https://biometris.github.io/statgenSTA/) R package. The "DSBrAPP" tool is a decision support tool helping breeders to select germplasm according to their various characteristics and save this germplasm list into BMS. Finally, the "[snpclust](https://github.com/jframi/snpclust)" tool enables a user to check and manually correct the clustering of fluorescence based SNP genotyping data. diff --git a/content/03.06.03.BrAPIMapper.md b/content/03.06.01.BrAPIMapper.md similarity index 100% rename from content/03.06.03.BrAPIMapper.md rename to content/03.06.01.BrAPIMapper.md diff --git a/content/03.06.01.MIAPPE_MIRA.md b/content/03.06.01.MIAPPE_MIRA.md deleted file mode 100644 index c0337d9..0000000 --- a/content/03.06.01.MIAPPE_MIRA.md +++ /dev/null @@ -1,6 +0,0 @@ -#### MIRA - - -In the plant phenotyping community, [MIAPPE](https://www.miappe.org/) (Minimal Information About a Plant Phenotyping Experiment) [@doi:10.1111/nph.16544] is an established standard for phenotyping experiments. It is commonly serialized into the ISA-Tab file type. [@doi:10.1038/ng.1054] Although ISA-Tab is easy to read for non-technical experts due to its file-based approach, it lacks programmatic accessibility, particularly for web applications. BrAPI, which is aligned with MIAPPE, can help solve this problem. - -[MIRA](https://github.com/USDA-ARS-GBRU/SugarcaneCrossingTool) is a tool that enables the automatic deployment of a BrAPI server on a MIAPPE-compliant dataset in ISA-Tab format. It can be deployed from a Docker image with the dataset mounted. By utilizing the mapping between MIAPPE, ISA-Tab, and BrAPI, there is no need for parsing or manual mapping of datasets that are already compliant with (meta-)data standards. By gaining programmatic access through BrAPI to these datasets, it facilitates the integration of phenotyping datasets into web applications. diff --git a/content/03.06.02.MIAPPE.md b/content/03.06.02.MIAPPE.md new file mode 100644 index 0000000..e1ed108 --- /dev/null +++ b/content/03.06.02.MIAPPE.md @@ -0,0 +1,8 @@ +#### MIRA and BrAPI2ISA + + +Since the release of BrAPI 1.3, efforts have been made to incorporate support for the [MIAPPE](https://www.miappe.org/) (Minimal Information About a Plant Phenotyping Experiment) [@doi:10.1111/nph.16544] standard into the specification, achieving full compatibility in BrAPI 2.0. Consequently, BrAPI now includes all attributes necessary for MIAPPE compliance, adhering to standardized descriptions in accordance with MIAPPE guidelines. In some communities and projects, phenotyping data and metadata are archived and published as structured ISA-Tab files, validated using the [MIAPPE ISA configuration](https://github.com/ELIXIR-Belgium/isatab-validation) [@doi:10.1038/ng.1054]. Although ISA-Tab is easy to read for non-technical experts due to its file-based approach, it lacks programmatic accessibility, particularly for web applications. + +[MIRA](https://github.com/USDA-ARS-GBRU/SugarcaneCrossingTool) enables the automatic deployment of a BrAPI server on a MIAPPE-compliant dataset in ISA-Tab format, facilitating programmatic access to these datasets. It is deployable from a Docker image with the dataset mounted. The tool leverages the mapping between MIAPPE, ISA-Tab, and BrAPI, eliminating the need for parsing or manual mapping of datasets compliant with (meta-)data standards. By providing programmatic access through BrAPI, MIRA facilitates the integration of phenotyping datasets into web applications. + +The [BrAPI2ISA](https://github.com/elixir-europe/plant-brapi-to-isa) service functions as a converter between a BrAPI-compatible server and the ISA-Tab format. The tool simplifies, automates, and facilitates the archiving of data, thereby enhancing data preservation and accessibility. The BrAPI2ISA tool is compatible with BrAPI 1.3 and welcomes community contributions to support the latest versions of BrAPI. diff --git a/content/03.06.02.MIAPPE_BrAPI2ISA.md b/content/03.06.02.MIAPPE_BrAPI2ISA.md deleted file mode 100644 index d6fc896..0000000 --- a/content/03.06.02.MIAPPE_BrAPI2ISA.md +++ /dev/null @@ -1,6 +0,0 @@ -#### BrAPI2ISA - - -Since the release of BrAPI 1.3, efforts have been made to incorporate support for the MIAPPE standard into the specification [@doi:10.1111/nph.16544]. This integration was finalized in BrAPI 2.0, resulting in full compatibility between the two standards. Consequently, BrAPI now encompasses all attributes necessary for MIAPPE compliance, adhering to standardized descriptions in accordance with MIAPPE guidelines. - -In some communities and projects, phenotyping data and metadata is archived and published as structured ISA-Tab files, and validated using the [MIAPPE ISA configuration](https://github.com/ELIXIR-Belgium/isatab-validation). The [BrAPI2ISA](https://github.com/elixir-europe/plant-brapi-to-isa) service functions as a converter between a BrAPI compatible server and the ISA-Tab format. This simplifies, automates, and facilitates the archiving of data, thereby enhancing data preservation and accessibility. The BrAPI2ISA tool is designed to be compatible with BrAPI 1.3, and is open to contributions from the community to extend support for the latest versions of BrAPI. diff --git a/content/03.06.04.Zendro.md b/content/03.06.03.Zendro.md similarity index 100% rename from content/03.06.04.Zendro.md rename to content/03.06.03.Zendro.md diff --git a/content/04.discussion.md b/content/04.discussion.md index bae60f1..f6e534d 100644 --- a/content/04.discussion.md +++ b/content/04.discussion.md @@ -12,11 +12,11 @@ ### BrAPI for Breeders and Scientists -The BrAPI technical specification document is meant to be read and used by software developers. However, the purpose of the specification, and the community around it, is to make things faster, easier, and cheaper for the breeders and scientists working on breeding and other agricultural projects. BrAPI offers a convenient path to automation, interoperability, and data integration for software tools in the breeding domain. All of the software described above could be made interoperable with manual effort, moving and editing data files by hand from tool to tool. However, when the basic structure and flow of data becomes automated, breeders and scientists can spend less time on data management and more time focusing on the science. For many, the ultimate goal is the development of a digital ecosystem: a collection of software tools and applications that can all work together seamlessly. In this digital ecosystem, data is collected digitally from the beginning, reducing as much human error as possible. The data is checked by quality control and stored automatically, then it can be sent to any internal tool or external lab for further analysis with just the click of a button. This idea might sound too good to be true, but as more tools start sharing a universal data standard, automating data flow becomes easier, and the community gets closer to total interoperability. +The BrAPI technical specification document is meant to be read and used by software developers. However, the purpose of the specification, and the community around it, is to make things faster, easier, and cheaper for the breeders and scientists working on breeding, genetics, phenomics and other agricultural projects. BrAPI offers a convenient path to automation, interoperability, and data integration for software tools in those domains. All of the software described above could be made interoperable with manual effort, moving and editing data files by hand from tool to tool. However, when the basic structure and flow of data becomes automated, breeders and scientists can spend less time on data management and more time focusing on the science. For many, the ultimate goal is the development of a digital ecosystem: a collection of software tools and applications that can all work together seamlessly. In this ideal digital ecosystem, data is collected digitally from the beginning, reducing as much human error as possible. The data is checked by quality control and stored automatically, then it can be sent to any internal tool or external lab for further analysis with just the click of a button. This idea might sound too good to be true, but as more tools start sharing a universal data standard, automating data flow becomes easier, and the community gets closer to total interoperability. ### Looking Ahead -The BrAPI specification will continue to grow, enabling more use cases and new types of data. These new use cases might include newer scientific techniques and technologies. Things like drone imaging data, spectroscopy, LIDAR, metabolomics, transcriptomics, high-throughput phenotyping, pan genomes, and machine learning analysis. All of these technologies can open new avenues for research and development of new crop varieties. All of these technologies also generate more data, and require data sharing between different software applications and data repositories. The BrAPI project leadership and community is committed to building the standards to support these new use cases as they arrive and become accepted by the scientific community. In fact, small groups within the BrAPI community have already start building generic data models and proposed communication standards for many of the technologies listed above. These community efforts will eventually become part of the BrAPI standard in a future version of the specification document. +The BrAPI specification will continue a controlled and necessary growth. It will enable more use cases and new types of data that might include newer scientific techniques and technologies. There will be careful evaluation of the relevance of using REST web service approaches, and in particular BrAPI, for things like drone imaging data, spectroscopy, LIDAR, metabolomics, transcriptomics, high-throughput phenotyping, pan genomes, and machine learning analysis. All of these technologies can open new avenues for research and development of new crop varieties. However, they will also generate more data, more types of data, and require data sharing between different software applications and data repositories. The BrAPI project leadership and community is committed to building the standards to support these new use cases as they arrive and become accepted by the scientific community. In fact, small groups within the BrAPI community have already start building generic data models and proposed communication standards for many of the technologies listed above. These community efforts will eventually become part of the BrAPI standard in a future version of the specification document. Expanding the BrAPI specification is important for the community, however it is just as important not to reinvent or compete with existing functional standards. Additions to the BrAPI specification are reviewed thoroughly by the community to make sure BrAPI is compliant with existing standards and data structures. For example, the community has requested compliance with the GFF3 standard for genomic data and the GeoTIFF standard for aerial image data. Pieces of these existing popular data structures might be integrated into the overall BrAPI standard documentation. In some cases, BrAPI will only reference other standards instead of including them in the specification. For example, there have been community discussions around developing connections with the NOAA CDO standard for weather data, or the Galaxy Analytics API for analytics pipeline controls and information. These standards are perfectly adequate on their own, recreating them in the BrAPI standard would be redundant and outside the main scope of the project. diff --git a/content/05.methods.md b/content/05.methods.md index 7b570f7..d65a794 100644 --- a/content/05.methods.md +++ b/content/05.methods.md @@ -4,6 +4,6 @@ The BrAPI Project day-to-day operations are run by the BrAPI Project Coordinator The standard documentation is stored and maintained in a GitHub repository. The core documentation is written using the OpenAPI 3 documentation standard, and can be easily transformed into other documentation standards as needed. Copies of the core documentation is published on the API documentation sites Apiary and SwaggerHub for maximum accessability by the community. All these online locations are public, and the specification has a standard MIT open source license. -New versions of the BrAPI standard are developed periodically based on community demand. Problems and enhancements are collected by the community over time, then implemented together to create a new stable version. This ensures the standard stays up=to-date and relevant to the community, but maintains a certain level of stability over time. Minor version updates are designed to be backward compatible to further enhance the stability of the standard. +New versions of the BrAPI standard are developed periodically based on community demand. Problems and enhancements are collected by the community over time, tracked in GitHub issues, then implemented together to create a new stable version. This ensures the standard stays up-to-date and relevant to the community, but maintains a certain level of stability over time. Minor version updates are designed to be backward compatible to further enhance the stability of the standard. -As discussed above, hackathon events are the primary way the BrAPI community stays together. One in-person and one virtual event per year maintain project momentum and social comradery. The hackathons provide a dedicated time to discuss issues and enhancements with the specification, particular use cases, and generally how the community is run and organized. For some groups, the BrAPI hackathon provide the only time they have to work on BrAPI related implementations, due to other constraints in their normal work. +As discussed above, hackathon events are the primary way the BrAPI community stays together. One in-person and one virtual event per year maintain project momentum and social comradery. The hackathons provide a dedicated time to discuss issues and enhancements with the specification, particular use cases, and generally how the community is run and organized. diff --git a/content/90.back-matter.md b/content/90.back-matter.md index 799eebc..3c5c4e8 100644 --- a/content/90.back-matter.md +++ b/content/90.back-matter.md @@ -15,19 +15,22 @@ The authors declare no competing interests. The authors would like to acknowledge the following funding sources: + The BrAPI Project is funded by the USDA grant NIFA-DSFAS 2022-67021-37024. -+ Innovation Lab for Crop Improvement Cornell, FFAR ++ Innovation Lab for Crop Improvement Cornell, ILCI: 7200AA19LE00005, FFAR: CA20-SS-0000000103 + The AGENT project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 862613. -+ This work was supported by the Agence Nationale de la Recherche, programme Investissements d'avenir, ANR-11-INBS-0012 (Phenome) + USDA National Institute of Food and Agriculture National Research Support Project 10 + USDA NIFA: 2022-51181-38449 ++ This work was supported by the Agence Nationale de la Recherche, programme Investissements d'avenir, ANR-11-INBS-0012 (Phenome) + U.S. Department of Agriculture, under agreement numbers [8062-21000-043-004-A, 8062-21000-052-002-A, and 8062-21000-052-003-A] + The Bill and Malinda Gates Foundation in cooperation with the Excellence in Breeding Platform of the CGIAR ++ This work was funded by ELIXIR, the research infrastructure for life science data. ++ This work was supported by ELIXIR, the research infrastructure for life science data. ++ The French Networks of Biological Resource Centres for Agricultural, Environmental and Life Sciences, doi: 10.15454/b4ec-tf49 ## Author Contributions {.page_break_before} + **Peter Selby**: Peter wrote and edited a significant portion of the manuscript. Peter has been the BrAPI Project Coordinator since 2017, managing the day-to-day operations of the BrAPI project. + **Rafael Abbeloos**: Rafael wrote the section on the PIPPA project, and has made significant contributions to the BrAPI compatibility of the PIPPA project. -+ **Anne-Francoise Adam-Blondon**: Anne-Francoise has made significant contributions to the Florilège and Faidare projects. ++ **Anne-Francoise Adam-Blondon**: Anne-Francoise has made significant contributions to the Florilège, FAIDARE and AGENT projects. + **Francisco J. Agosto-Pérez**: Francisco wrote the section on the ImageBreed project, and has made significant contributions to the BrAPI compatibility of the ImageBreed project. + **Michael Alaux**: Michael has made significant contributions to the AGENT project. + **Isabelle Alic**: Isabelle wrote the section on the PHIS project, and has made significant contributions to the BrAPI compatibility of the PHIS project. @@ -46,7 +49,8 @@ The authors would like to acknowledge the following funding sources: + **Mariano Crimi**: Mariano has made significant contributions to the BMS project. + **Gouripriya Davuluri**: Gouripriya has made significant contributions to the AGENT project. + **Kauê de Sousa**: Kauê wrote the section on the ClimMob project, and has made significant contributions to the BrAPI compatibility of the ClimMob project. -+ **Jeremy Destin**: Jeremy has made significant contributions to the Florilège and Faidare projects. ++ **Jeremy Destin**: Jeremy has made significant contributions to the FAIDARE project. ++ **Stijn Dhondt**: Stijn has made significant contributions to the PIPPA project. + **Ajay Dhungana**: Ajay wrote the section introductions for the Phenotyping, Genotyping, and Germplasm topics. + **Bert Droesbeke**: Bert wrote the section on the BrAPI2ISA project, and has made significant contributions to the BrAPI compatibility of the BrAPI2ISA project. + **Manuel Feser**: Manuel wrote the section on the MIRA project, and has made significant contributions to the BrAPI compatibility of the MIRA project. @@ -69,10 +73,10 @@ The authors would like to acknowledge the following funding sources: + **Erwan Le-Floch**: Erwan has made significant contributions to the AGENT project. + **Francisco López**: Francisco has made significant contributions to the GLIS project. + **Brandon Madriz**: Brandon wrote the section on the ClimMob project, and has made significant contributions to the BrAPI compatibility of the ClimMob project. -+ **Dorrie Main**: Dorrie has made significant contributions to the BIMS project. ++ **Dorrie Main**: Dorrie has made significant contributions to the BIMS project. + **Marco Marsella**: Marco wrote the section on the GLIS project, and has made significant contributions to the BrAPI compatibility of the GLIS project. -+ **Maud Marty**: Maud has made significant contributions to the Florilège and Faidare projects. -+ **Célia Michotey**: Célia has made significant contributions to the Florilège and Faidare projects. ++ **Maud Marty**: Maud has made significant contributions to the FAIDARE project. ++ **Célia Michotey**: Célia has made significant contributions to the Florilège and FAIDARE projects. + **Zachary Miller**: Zachary has made significant contributions to the PHG project. + **Iain Milne**: Iain has made significant contributions to the Flapjack, Gridscore, and Germinate projects. + **Lukas A. Mueller**: Lukas wrote the section on the Breedbase project, and has made significant contributions to the BrAPI compatibility of the Breedbase project. Lukas is also Co-PI on the current BrAPI Grant and has supported the BrAPI project since its inception. @@ -80,10 +84,10 @@ The authors would like to acknowledge the following funding sources: + **Pascal Neveu**: Pascal has made significant contributions to the PHIS project. + **Nick Palladino**: Nick has made significant contributions to the DeltaBreed project. + **Tim Parsons**: Tim has made significant contributions to the DeltaBreed project. -+ **Cyril Pommier**: Cyril wrote the section on the Faidare projects, and has made significant contributions to the BrAPI compatibility of the Florilège, Faidare, and AGENT projects. ++ **Cyril Pommier**: Cyril wrote the section on the FAIDARE projects, and has made significant contributions to the BrAPI compatibility of the Florilège, FAIDARE, and AGENT projects. Cyril has also made significant contributions to the Germplasm and Phenotyping modules of the BrAPI specification. + **Jean-François Rami**: Jean-François wrote the section on the ShinyBrAPPs project, and has made significant contributions to the BrAPI compatibility of the ShinyBrAPPs project. + **Sebastian Raubach**: Sebastian wrote the section on the GridScore and Germinate projects, and has made significant contributions to the BrAPI compatibility of the GridScore, Germinate, and Flapjack projects. -+ **Trevor Rife**: Trevor has made significant contributions to the Field Book project. ++ **Trevor Rife**: Trevor wrote the section on the Field Book project, and has made significant contributions to the BrAPI compatibility of the Field Book project. + **Kelly Robbins**: Kelly is the PI for the BrAPI Project grant, and has supported all technical and scientific aspects of the project. + **Mathieu Rouard**: Mathieu wrote the section on the MGIS and GIGWA projects, and has made significant contributions to the BrAPI compatibility of the MGIS and GIGWA projects. + **Joseph Ruff**: Joseph has made significant contributions to the AGENT project. @@ -97,7 +101,7 @@ The authors would like to acknowledge the following funding sources: + **Grzegorz Uszynski**: Grzegorz wrote the section on the DArT Sample Submission project, and has made significant contributions to the BrAPI compatibility of the DArT Sample Submission project. + **Vivian Bass Vega**: Vivian has made significant contributions to the Zendro project. + **Stephan Weise**: Stephan has made significant contributions to the AGENT project. -+ **Shawn C. Yarnes**: Shawn wrote the section on the DeltaBreed project, and has made significant contributions to the BrAPI compatibility of the DeltaBreed project. ++ **Shawn C. Yarnes**: Shawn wrote the section on the DeltaBreed project, and has made significant contributions to the BrAPI compatibility of the DeltaBreed project. ## References {.page_break_before} diff --git a/content/images/BrAPI_Application_Chart.pdf b/content/images/BrAPI_Application_Chart.pdf index 1dc2ed4..eaf5098 100644 Binary files a/content/images/BrAPI_Application_Chart.pdf and b/content/images/BrAPI_Application_Chart.pdf differ diff --git a/content/images/BrAPI_Application_Chart.xlsx b/content/images/BrAPI_Application_Chart.xlsx index c0cbed3..7eb014e 100644 Binary files a/content/images/BrAPI_Application_Chart.xlsx and b/content/images/BrAPI_Application_Chart.xlsx differ diff --git a/content/images/BrAPI_Application_Chart_2.log b/content/images/BrAPI_Application_Chart_2.log deleted file mode 100644 index 26483b8..0000000 --- a/content/images/BrAPI_Application_Chart_2.log +++ /dev/null @@ -1,11 +0,0 @@ -%%[ ProductName: Distiller ]%% -Aptos-Narrow-Bold not found, using Courier. -%%[ Error: invalidfont; OffendingCommand: yshow ]%% - -Stack: -[90 71 74 71 47 70 45 35 73 74 0] -( ) - - -%%[ Flushing: rest of job (to end-of-file) will be ignored ]%% -%%[ Warning: PostScript error. 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Agosto-Pérez initials: FJAP github: agostof orcid: 0000-0002-5059-253X email: fja32@cornell.edu + corresponding: false affiliations: - Cornell University funders: @@ -46,9 +49,10 @@ authors: initials: MA orcid: 0000-0001-9356-4072 email: michael.alaux@inrae.fr + corresponding: false affiliations: - - Université Paris-Saclay, INRAE, Bioinformatics, Plant Bioinformatics Facility, Versailles, France - - Université Paris-Saclay, INRAE, URGI, Versailles, France + - Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France + - Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France funders: - The AGENT project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 862613. - name: Isabelle Alic @@ -56,30 +60,33 @@ authors: github: Isabelle-inrae orcid: 0000-0002-8961-6068 email: isabelle.alic@inrae.fr + corresponding: false affiliations: - MISTEA, University of Montpellier, INRAE, Institut Agro, Montpellier, France - funders: - - This work was supported by the Agence Nationale de la Recherche, programme Investissements d'avenir, ANR-11-INBS-0012 (Phenome) - name: Khaled Al-Shamaa initials: KS github: khaled-alshamaa orcid: 0000-0002-7668-3798 email: k.el-shamaa@cgiar.org + corresponding: false affiliations: - ICARDA - name: Johan Steven Aparicio initials: JSA email: J.Aparicio@cgiar.org + corresponding: false affiliations: - Alliance Bioversity-CIAT - name: Jan Erik Backlund initials: JEB email: j.backlund@integratedbreeding.net + corresponding: false affiliations: - Integrated Breeding Platform - name: Aldrin Batac initials: AB email: aldrin.batac@leafnode.io + corresponding: false affiliations: - Integrated Breeding Platform - Leafnode LLC @@ -88,22 +95,24 @@ authors: github: sebeier orcid: 0000-0002-2177-8781 email: s.beier@fz-juelich.de + corresponding: false affiliations: - 'Institute of Bio- and Geosciences (IBG-4: Bioinformatics), CEPLAS, Forschungszentrum Jülich GmbH, Wilhelm Johnen Straße, 52428 Jülich, Germany' - Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany - name: Gabriel Besombes initials: GB github: Gabriel-Besombes + orcid: 0009-0004-1359-2145 email: gabriel.besombes@inrae.fr + corresponding: false affiliations: - MISTEA, University of Montpellier, INRAE, Institut Agro, Montpellier, France - funders: - - This work was supported by the Agence Nationale de la Recherche, programme Investissements d'avenir, ANR-11-INBS-0012 (Phenome) - name: Alice Boizet initials: AB github: aliceboizet orcid: 0000-0003-4096-6689 email: alice.boizet@cirad.fr + corresponding: false affiliations: - CIRAD, UMR AGAP Institut, Montpellier, France. - AGAP Institut, CIRAD, INRAE, Institut Agro, Université de Montpellier, Montpellier, France. @@ -112,6 +121,7 @@ authors: github: matthijsbrouwer orcid: 0000-0001-8183-0484 email: matthijs.brouwer@wur.nl + corresponding: false affiliations: - Wageningen University and Research - name: Terry Casstevens @@ -119,6 +129,7 @@ authors: github: tcasstevens orcid: 0000-0001-7602-0487 email: tmc46@cornell.edu + corresponding: false affiliations: - Buckler Lab and Institute for Genomic Diversity, Cornell University - name: Arnaud Charleroy @@ -126,26 +137,28 @@ authors: github: niio972 orcid: 0009-0009-7575-3617 email: arnaud.charleroy@inrae.fr + corresponding: false affiliations: - MISTEA, University of Montpellier, INRAE, Institut Agro, Montpellier, France - funders: - - This work was supported by the Agence Nationale de la Recherche, programme Investissements d'avenir, ANR-11-INBS-0012 (Phenome) - name: Keo Corak initials: KC github: keocorak orcid: 0000-0002-4129-3319 email: kecorak@ncsu.edu + corresponding: false affiliations: - USDA-ARS Genomics and Bioinformatics Research Unit - name: Chaney Courtney initials: CC github: chaneylc email: ccourtn@clemson.edu + corresponding: false affiliations: - Clemson University - name: Mariano Crimi initials: MC email: m.crimi@integratedbreeding.net + corresponding: false affiliations: - Integrated Breeding Platform - name: Gouripriya Davuluri @@ -153,6 +166,7 @@ authors: github: Gouripriya5 orcid: 0009-0005-2308-0773 email: Gouripriya.Davuluri@leibniz-ipk.de + corresponding: false affiliations: - Leibniz Institute of Plant Genetics and Crop Plant Research funders: @@ -162,18 +176,28 @@ authors: github: kauedesousa orcid: 0000-0002-7571-7845 email: k.desousa@cgiar.org + corresponding: false affiliations: - Digital Inclusion, Bioversity International, Montpellier, France - name: Jeremy Destin initials: JD - email: jeremy.destin@inrae.fr - affiliations: - - Université Paris-Saclay, INRAE, Bioinformatics, Plant Bioinformatics Facility, Versailles, France - - Université Paris-Saclay, INRAE, URGI, Versailles, France + email: jeremy.destin3@gmail.com + corresponding: false + affiliations: + - Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France + - Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France + - name: Stijn Dhondt + initials: SD + orcid: 0000-0003-4402-2191 + email: stijn.dhondt@vib.be + corresponding: false + affiliations: + - VIB AgroIncubator - name: Ajay Dhungana initials: AD github: GenAjay email: adhung4@lsu.edu + corresponding: false affiliations: - Louisiana State University (LSU) - name: Bert Droesbeke @@ -181,6 +205,7 @@ authors: github: bedroesb orcid: 0000-0003-0522-5674 email: bert.droesbeke@vib.be + corresponding: false affiliations: - VIB Data Core - name: Manuel Feser @@ -188,6 +213,7 @@ authors: github: feserm orcid: 0000-0001-6546-1818 email: feser@ipk-gatersleben.de + corresponding: false affiliations: - Leibniz Institute of Plant Genetics and Crop Plant Research - Graduate School DILS, Bielefeld Institute for Bioinformatics Infrastructure (BIBI) @@ -196,6 +222,7 @@ authors: github: mflores202 orcid: 0000-0002-7759-1617 email: mrf252@cornell.edu + corresponding: false affiliations: - Boyce Thompson Institute - name: Valentin Guignon @@ -203,11 +230,13 @@ authors: github: guignonv orcid: 0000-0003-0903-6811 email: v.guignon@cgiar.org + corresponding: false affiliations: - Bioversity International, Parc Scientifique Agropolis II, 34397 Montpellier, France - name: Corina Habito initials: CH email: c.habito@integratedbreeding.net + corresponding: false affiliations: - Integrated Breeding Platform - name: Asis Hallab @@ -215,24 +244,28 @@ authors: github: asishallab orcid: 0000-0002-2421-5431 email: a.hallab@fz-juelich.de + corresponding: false affiliations: - Jülich research center, Institute of Bio- and Geosciences (IBG), Bioinformatics (IBG-4) and Bingen Technical University of Applied Sciences, Germany - name: Puthick Hok initials: PH github: puthick email: puthick@diversityarrays.com + corresponding: false affiliations: - Diversity Arrays Technology (DArT) - name: Lynn Carol Johnson initials: LCJ orcid: 0000-0001-8103-2722 email: lcj34@cornell.edu + corresponding: false affiliations: - Buckler Lab and Institute for Genomic Diversity, Cornell University - name: Sook Jung initials: SJ orcid: 0000-0003-3968-2769 email: sook_jung@wsu.edu + corresponding: false affiliations: - Department of Horticulture, Washington State University funders: @@ -240,6 +273,7 @@ authors: - name: Paul Kersey initials: PK email: P.Kersey@kew.org + corresponding: false affiliations: - Royal Botanic Gardens, Kew funders: @@ -247,6 +281,7 @@ authors: - name: Andrzej Kilian initials: AK email: zej@diversityarrays.com + corresponding: false affiliations: - Diversity Arrays Technology (DArT) - name: Patrick König @@ -254,6 +289,7 @@ authors: github: patrick-koenig orcid: 0000-0002-8948-6793 email: koenig@ipk-gatersleben.de + corresponding: false affiliations: - Leibniz Institute of Plant Genetics and Crop Plant Research funders: @@ -263,6 +299,7 @@ authors: github: sumankumar1 orcid: 0009-0005-5832-7190 email: Suman.Kumar@leibniz-ipk.de + corresponding: false affiliations: - Leibniz Institute of Plant Genetics and Crop Plant Research funders: @@ -271,6 +308,7 @@ authors: initials: JDLS github: jlamossweeney email: jdl232@cornell.edu + corresponding: false affiliations: - Cornell University - name: Laszlo Lang @@ -278,6 +316,7 @@ authors: github: LzLang orcid: 0009-0009-8936-4532 email: laszlo.lang@stud.th-bingen.de + corresponding: false affiliations: - Bingen Technical University of Applied Sciences, Berlinstraße 109, 55411 Bingen am Rhein, Germany - name: Matthias Lange @@ -285,6 +324,7 @@ authors: github: langeipk orcid: 0000-0002-4316-078X email: lange@ipk-gatersleben.de + corresponding: false affiliations: - Leibniz Institute of Plant Genetics and Crop Plant Research funders: @@ -294,6 +334,7 @@ authors: github: marieALaporte orcid: 0000-0002-8461-9745 email: m.a.laporte@cgiar.org + corresponding: false affiliations: - Digital Inclusion, Bioversity International, Montpellier, France. - name: Taein Lee @@ -301,30 +342,35 @@ authors: github: leetaei orcid: 0000-0002-2000-2586 email: taein_lee@hotmail.com + corresponding: false affiliations: - Department of Horticulture, Washington State University - name: Erwan Le-Floch initials: ELF + github: erlefloch + orcid: 0000-0002-1010-6859 email: erwan.le-floch@inrae.fr + corresponding: false affiliations: - - Université Paris-Saclay, INRAE, Bioinformatics, Plant Bioinformatics Facility, Versailles, France - - Université Paris-Saclay, INRAE, URGI, Versailles, France - funders: - - The AGENT project is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 862613. + - Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France + - Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France - name: Francisco López initials: FL email: francisco.lopez@fao.org + corresponding: false affiliations: - International Treaty on Plant Genetic Resources for Food and Agriculture, FAO - name: Brandon Madriz initials: BM email: bmadriz@mrbotcr.com + corresponding: false affiliations: - MrBot Software Solutions, Cartago, Costa Rica - - name: Dorrie Main + - name: Dorrie Main initials: DM orcid: 0000-0002-1162-2724 email: dorrie@wsu.edu + corresponding: false affiliations: - Department of Horticulture, Washington State University funders: @@ -333,26 +379,33 @@ authors: initials: MM orcid: 0000-0003-0334-8785 email: marco.marsella@fao.org + corresponding: false affiliations: - International Treaty on Plant Genetic Resources for Food and Agriculture, FAO - name: Maud Marty initials: MM + github: maudmarty + orcid: 0009-0001-8764-6466 email: maud.marty@inrae.fr + corresponding: false affiliations: - - Université Paris-Saclay, INRAE, Bioinformatics, Plant Bioinformatics Facility, Versailles, France - - Université Paris-Saclay, INRAE, URGI, Versailles, France + - Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France + - Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France - name: Célia Michotey initials: CM + github: cmichotey orcid: 0000-0003-1877-1703 email: celia.michotey@inrae.fr + corresponding: false affiliations: - - Université Paris-Saclay, INRAE, Bioinformatics, Plant Bioinformatics Facility, Versailles, France - - Université Paris-Saclay, INRAE, URGI, Versailles, France + - Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France + - Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France - name: Zachary Miller initials: ZM github: zrm22 orcid: 0000-0002-5454-4527 email: zrm22@cornell.edu + corresponding: false affiliations: - Buckler Lab and Institute for Genomic Diversity, Cornell University - name: Iain Milne @@ -360,6 +413,7 @@ authors: github: imilne orcid: 0000-0002-4126-0859 email: iain.milne@hutton.ac.uk + corresponding: false affiliations: - The James Hutton Institute - name: Lukas A. Mueller @@ -367,12 +421,14 @@ authors: github: lukasmueller orcid: 0000-0001-8640-1750 email: lam87@cornell.edu + corresponding: false affiliations: - The Boyce Thompson Institute - name: Moses Nderitu initials: MN github: jndmose email: m.nderitu@seqart.net + corresponding: false affiliations: - SEQART AFRICA - name: Pascal Neveu @@ -380,6 +436,7 @@ authors: github: pascalneveu orcid: 0000-0003-4189-7793 email: pascal.neveu@inrae.fr + corresponding: false affiliations: - MISTEA, University of Montpellier, INRAE, Institut Agro, Montpellier, France funders: @@ -389,6 +446,7 @@ authors: github: nickpalladino orcid: 0009-0009-1645-297X email: np398@cornell.edu + corresponding: false affiliations: - Breeding Insight, Cornell University funders: @@ -398,6 +456,7 @@ authors: github: timparsons orcid: 0000-0001-6209-2455 email: tim@timparsons.io + corresponding: false affiliations: - Breeding Insight, Cornell University funders: @@ -407,16 +466,16 @@ authors: github: cpommier orcid: 0000-0002-9040-8733 email: cyril.pommier@inrae.fr + corresponding: false affiliations: - - Université Paris-Saclay, INRAE, Bioinformatics, Plant Bioinformatics Facility, Versailles, France - - Université Paris-Saclay, INRAE, URGI, Versailles, France - funders: - - This work was supported by the Agence Nationale de la Recherche, programme Investissements d'avenir, ANR-11-INBS-0012 (Phenome) + - Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France + - Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France - name: Jean-François Rami initials: JFR github: jframi orcid: 0000-0002-5679-3877 email: jean-francois.rami@cirad.fr + corresponding: false affiliations: - CIRAD, UMR AGAP Institut, Montpellier, France. - AGAP Institut, CIRAD, INRAE, Institut Agro, Université de Montpellier, Montpellier, France. @@ -425,6 +484,7 @@ authors: github: sebastian-raubach orcid: 0000-0001-5659-247X email: sebastian.raubach@hutton.ac.uk + corresponding: false affiliations: - The James Hutton Institute - name: Trevor Rife @@ -432,26 +492,30 @@ authors: github: trife orcid: 0000-0002-5974-6523 email: twrife@clemson.edu + corresponding: false affiliations: - Clemson University - name: Kelly Robbins initials: KR orcid: 0000-0001-9522-9585 email: krr73@cornell.edu + corresponding: false affiliations: - Cornell University funders: - - The Bill and Malinda Gates Foundation in cooperation with the Excellence in Breeding Platform of the CGIAR + - The BrAPI Project is funded by the USDA grant NIFA-DSFAS 2022-67021-37024. - name: Mathieu Rouard initials: MR github: mrouard orcid: 0000-0003-0284-1885 email: m.rouard@cgiar.org + corresponding: false affiliations: - Bioversity International, Parc Scientifique Agropolis II, 34397 Montpellier, France - name: Joseph Ruff initials: JR email: J.Ruff@kew.org + corresponding: false affiliations: - Royal Botanic Gardens, Kew funders: @@ -461,6 +525,7 @@ authors: github: GuilhemSempere orcid: 0000-0001-7429-2091 email: guilhem.sempere@cirad.fr + corresponding: false affiliations: - CIRAD, UMR INTERTRYP, Montpellier, France INTERTRYP, Univ Montpellier, CIRAD, IRD, Montpellier, France French Institute of Bioinformatics (IFB) - South Green Bioinformatics Platform, Bioversity, CIRAD, INRAE, IRD, Montpellier, France @@ -468,6 +533,7 @@ authors: initials: RMS github: romil2807 email: rmshah3@ncsu.edu + corresponding: false affiliations: - North Carolina State University - name: Paul Shaw @@ -475,6 +541,7 @@ authors: github: cardinalb orcid: 0000-0002-0202-1150 email: paul.shaw@hutton.ac.uk + corresponding: false affiliations: - The James Hutton Institute - name: Becky Smith @@ -482,11 +549,13 @@ authors: github: Batbaby91 orcid: 0000-0002-8968-3383 email: becky.smith@hutton.ac.uk + corresponding: false affiliations: - The James Hutton Institute - name: Nahuel Soldevilla initials: NS email: n.soldevilla@integratedbreeding.net + corresponding: false affiliations: - Integrated Breeding Platform - Leafnode LLC @@ -495,13 +564,13 @@ authors: github: annetireau orcid: 0000-0001-8501-6922 email: anne.tireau@inrae.fr + corresponding: false affiliations: - MISTEA, University of Montpellier, INRAE, Institut Agro, Montpellier, France - funders: - - This work was supported by the Agence Nationale de la Recherche, programme Investissements d'avenir, ANR-11-INBS-0012 (Phenome) - name: Clarysabel Tovar initials: CT email: clarysabel@leafnode.io + corresponding: false affiliations: - Integrated Breeding Platform - Leafnode LLC @@ -509,6 +578,7 @@ authors: initials: GU github: grzegorz69 email: grzegorz@diversityarrays.com + corresponding: false affiliations: - Diversity Arrays Technology (DArT) - name: Vivian Bass Vega @@ -516,12 +586,14 @@ authors: github: VivianBass orcid: 0009-0002-2476-9888 email: vivbass4@gmail.com + corresponding: false affiliations: - Bingen Technical University of Applied Sciences, Berlinstraße 109, 55411 Bingen am Rhein, Germany - name: Stephan Weise initials: SW orcid: 0000-0003-4031-9131 email: weise@ipk-gatersleben.de + corresponding: false affiliations: - Leibniz Institute of Plant Genetics and Crop Plant Research funders: @@ -531,6 +603,7 @@ authors: github: SCYarnes orcid: 0000-0002-1090-0403 email: scy32@cornell.edu + corresponding: false affiliations: - Breeding Insight, Cornell University funders: diff --git a/content/supplemental_docs/BrAPI_Community.yaml b/content/supplemental_docs/BrAPI_Community.yaml index 285815d..a72ce4e 100644 --- a/content/supplemental_docs/BrAPI_Community.yaml +++ b/content/supplemental_docs/BrAPI_Community.yaml @@ -21,16 +21,16 @@ community: initials: AFAB orcid: 0000-0002-3412-9086 affiliations: - - Université Paris-Saclay, INRAE, Bioinformatics, Plant Bioinformatics Facility, Versailles, France - - Université Paris-Saclay, INRAE, URGI, Versailles, France + - Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France + - Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France - name: Francisco Agosto - name: Michael Alaux email: michael.alaux@inrae.fr initials: MA orcid: 0000-0001-9356-4072 affiliations: - - Université Paris-Saclay, INRAE, Bioinformatics, Plant Bioinformatics Facility, Versailles, France - - Université Paris-Saclay, INRAE, URGI, Versailles, France + - Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France + - Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France - name: Isabelle Alic initials: IA github: Isabelle-inrae @@ -38,8 +38,6 @@ community: email: isabelle.alic@inrae.fr affiliations: - INRAE - funders: - - This work was supported by the Agence Nationale de la Recherche, programme Investissements d'avenir, ANR-11-INBS-0012 (Phenome) - name: Khaled Al-Shamaa initials: KS github: khaled-alshamaa @@ -85,9 +83,10 @@ community: affiliations: - Cornell University - name: Chaney Courtney - github: chaneylc initials: CC email: ccourtn@clemson.edu + github: chaneylc + orcid: 0009-0004-7273-1280 affiliations: - Clemson University - name: Mariano Crimi @@ -101,11 +100,11 @@ community: affiliations: - Leibniz Institute of Plant Genetics and Crop Plant Research - name: Jeremy Destin - email: jeremy.destin@inrae.fr + email: jeremy.destin3@gmail.com initials: JD affiliations: - - Université Paris-Saclay, INRAE, Bioinformatics, Plant Bioinformatics Facility, Versailles, France - - Université Paris-Saclay, INRAE, URGI, Versailles, France + - Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France + - Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France - name: Bert Droesbeke initials: BD github: bedroesb @@ -221,9 +220,11 @@ community: - name: Erwan Le-Floch email: erwan.le-floch@inrae.fr initials: ELF + github: erlefloch + orcid: 0000-0002-1010-6859 affiliations: - - Université Paris-Saclay, INRAE, Bioinformatics, Plant Bioinformatics Facility, Versailles, France - - Université Paris-Saclay, INRAE, URGI, Versailles, France + - Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France + - Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France - name: Francisco López initials: FL email: francisco.lopez@fao.org @@ -237,17 +238,20 @@ community: - International Treaty on Plant Genetic Resources for Food and Agriculture, FAO - name: Maud Marty email: maud.marty@inrae.fr + github: maudmarty + orcid: 0009-0001-8764-6466 initials: MM affiliations: - - Université Paris-Saclay, INRAE, Bioinformatics, Plant Bioinformatics Facility, Versailles, France - - Université Paris-Saclay, INRAE, URGI, Versailles, France + - Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France + - Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France - name: Célia Michotey email: celia.michotey@inrae.fr initials: CM + github: cmichotey orcid: 0000-0003-1877-1703 affiliations: - - Université Paris-Saclay, INRAE, Bioinformatics, Plant Bioinformatics Facility, Versailles, France - - Université Paris-Saclay, INRAE, URGI, Versailles, France + - Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France + - Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France - name: Zachary Miller initials: ZM email: zrm22@cornell.edu @@ -269,10 +273,8 @@ community: orcid: 0000-0002-9040-8733 email: cyril.pommier@inrae.fr affiliations: - - Université Paris-Saclay, INRAE, Bioinformatics, Plant Bioinformatics Facility, Versailles, France - - Université Paris-Saclay, INRAE, URGI, Versailles, France - funders: - - This work was supported by the Agence Nationale de la Recherche, programme Investissements d'avenir, ANR-11-INBS-0012 (Phenome) + - Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, 78026, Versailles, France + - Université Paris-Saclay, INRAE, URGI, 78026, Versailles, France - name: Sebastian Raubach initials: SR github: sebastian-raubach @@ -282,9 +284,9 @@ community: - The James Hutton Institute - name: Trevor Rife initials: TR + email: twrife@clemson.edu github: trife orcid: 0000-0002-5974-6523 - email: twrife@clemson.edu affiliations: - Clemson University - name: Mathieu Rouard